Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)

Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) 

The Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) certification is designed for professionals who want to validate their expertise in data integration, transformation, pipeline optimization, and workload maintenance using Azure Databricks. This certification focuses on building scalable data engineering solutions while applying data quality, governance, and Unity Catalog best practices. It is ideal for data engineers working with SQL, Python, Delta Lake, Unity Catalog, and enterprise-grade Azure data pipelines.

Knowledge Gained

By preparing for the DP-750 certification, you will gain expertise in:

• Configuring Azure Databricks workspaces and compute
• Governing data with Unity Catalog
• Building SQL and Python-based ETL pipelines
• Implementing Delta Lake and medallion architecture
• Optimizing data processing workloads
• Monitoring performance with Azure Monitor
• Managing Git-based SDLC and deployment workflows
• Securing data engineering environments with Microsoft Entra

Skills Required

To succeed in DP-750, candidates should be comfortable with:

• SQL and Python
• Azure Databricks notebooks and workflows
• Delta Lake concepts
• Unity Catalog governance
• Azure Data Factory
• Azure Monitor
• Microsoft Entra fundamentals
• Git and SDLC workflows
• Data modeling and transformation best practices

Who should take the DP-750 Exam?

This certification is best suited for:

• Data Engineers
• Azure Data Platform Engineers
• Databricks Developers
• ETL / ELT Engineers
• Analytics Engineers
• Lakehouse Architects
• Professionals working on Delta Lake and medallion pipelines

Course Outline

The Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750) Exam covers the following topics - 

• Domain 1 - Set up and configure an Azure Databricks environment (15–20%)
• Domain 2 - Secure and govern Unity Catalog objects (15–20%)
• Domain 3 - Prepare and process data (30–35%)
• Domain 4 - Deploy and maintain data pipelines and workloads (30–35%)